Quantile Forecasting of PM10 Data in Korea Based on Time Series Models

Abstract

In this chapter, we analyze the particulate matter PM10 data in Korea using time series models. For this task, we use the log-transformed data of the daily averages of the PM10 values collected from Korea Meteorological Administration and obtain an optimal ARMA model. We then conduct the entropy-based goodness of fit test for the obtained residuals to check the departure from the normal and skew-t distributions. Based on the selected skew-t ARMA model, we obtain conditional quantile forecasts using the parametric and quantile regression methods. The obtained result has a potential usage as a guideline for the patients with some respiratory disease to pay more attention to health care when the conditional quantile forecast is beyond the limit values of severe health hazards.